{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "packs loaded\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "#from tensorflow.examples.tutorials.mnist import input_data\n",
    "import input_data\n",
    "\n",
    "print (\"packs loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download and Extract MNIST dataset\n",
      "Extracting data/train-images-idx3-ubyte.gz\n",
      "Extracting data/train-labels-idx1-ubyte.gz\n",
      "Extracting data/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/t10k-labels-idx1-ubyte.gz\n",
      " tpye of 'mnist' is <class 'tensorflow.contrib.learn.python.learn.datasets.base.Datasets'>\n",
      " number of trian data is 55000\n",
      " number of test data is 10000\n"
     ]
    }
   ],
   "source": [
    "print (\"Download and Extract MNIST dataset\")\n",
    "mnist = input_data.read_data_sets('data/', one_hot=True)\n",
    "print\n",
    "print (\" tpye of 'mnist' is %s\" % (type(mnist)))\n",
    "print (\" number of trian data is %d\" % (mnist.train.num_examples))\n",
    "print (\" number of test data is %d\" % (mnist.test.num_examples))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What does the data of MNIST look like?\n",
      " type of 'trainimg' is <class 'numpy.ndarray'>\n",
      " type of 'trainlabel' is <class 'numpy.ndarray'>\n",
      " type of 'testimg' is <class 'numpy.ndarray'>\n",
      " type of 'testlabel' is <class 'numpy.ndarray'>\n",
      " shape of 'trainimg' is (55000, 784)\n",
      " shape of 'trainlabel' is (55000, 10)\n",
      " shape of 'testimg' is (10000, 784)\n",
      " shape of 'testlabel' is (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "# What does the data of MNIST look like? \n",
    "print (\"What does the data of MNIST look like?\")\n",
    "trainimg   = mnist.train.images\n",
    "trainlabel = mnist.train.labels\n",
    "testimg    = mnist.test.images\n",
    "testlabel  = mnist.test.labels\n",
    "print\n",
    "print (\" type of 'trainimg' is %s\"    % (type(trainimg)))\n",
    "print (\" type of 'trainlabel' is %s\"  % (type(trainlabel)))\n",
    "print (\" type of 'testimg' is %s\"     % (type(testimg)))\n",
    "print (\" type of 'testlabel' is %s\"   % (type(testlabel)))\n",
    "print (\" shape of 'trainimg' is %s\"   % (trainimg.shape,))\n",
    "print (\" shape of 'trainlabel' is %s\" % (trainlabel.shape,))\n",
    "print (\" shape of 'testimg' is %s\"    % (testimg.shape,))\n",
    "print (\" shape of 'testlabel' is %s\"  % (testlabel.shape,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How does the training data look like?\n",
      "23524th Training Data Label is 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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7UAZ4A09ejP27lAHZw2Cb5hB3UUR8Ocr1JYZKlKtbzWbF/biUMrZzWPYjwJzmkHdhRMxv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328e2c52e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328e371828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Al2bmLVMVrB7CkrQxmwnNEZK00TKEJakiQ1iSKjKEJakiQ1iSKjKEJakiQ1iS\nKvofr6mja1MLM/AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328e3c7400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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g15TS8yNrqiT1n3bHCdfqvkgpHQMc00F7JGlc8deWNSJbbrll7dq11lqrdu2d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\nwm/DnBsGFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328f6fb908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+qnpOktRiLNcTPgvYHdirS22RpAlnVHvCEXEmcACwT0rp7panVgIBbNf2ku2q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\nsCQVZAhLUkGGsCQVZAhLUkH/H4KUlN1SxXMzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328f737d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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K6FYLWPc8rqT07RyV8wiwT3PLe31EnNr+Ta5pYhvKlf7dMJLnbp3jazFlzt2UCmHKu9Zs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\nJ1UjSZpBHDtCkio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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328f786f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28456th Training Data Label is 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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Tlc3MG4DbgW1msixJGmc97wk3Abwv8NLM/G0X5bcA5gBThrUkrYl67Sd8EvBG\n4A3AyoiY2zzWb17fMCJOiIhdImLLiNgT+FfgWmDhoCsvSaOu1+aItwMbAT8Gbm15HNC8/giwI3Am\n8CvgS8DPgD0y8w8DqK8kjZVe+wlPGdqZ+SDwyr5qJElrEMeOkKSKDGFJqsgQlqSKDGFJqsgQlqSK\nDGFJqsgQlqSKDGFJqsgQlqSKDGFJqsgQlqSKDGFJqsgQlqSKDGFJqsgQlqSKDGFJqsgQlqSKDGFJ\nqsgQlqSKDGFJqsgQlqSKZkMIr1+7ApI0JNPm22wI4WfXroAkDcmzpysQmfkE1GOKCkTMAfYCbgQe\nrFoZSRqM9SkBvDAz75iqYPUQlqQ12WxojpCkNZYhLEkVGcKSVJEhLEkVGcKSVJEhLEkVGcKSVNH/\nB6VM2UW9sB4oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328fd43fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9502th Training Data Label is 9\n"
     ]
    },
    {
     "data": {
      "image/png": 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JKsgQlqSCvHaE1OM22GCD2rUTJkyoXTtv3ryRNEcd5p6wJBVkCEtSQYawJBVk\nCEtSQYawJBVkCEtSQYawJBVkCEtSQYawJBVkCEtSQW2dthwRnwbeC7wGeAK4Djg6pTS/oeZs4MCm\nh85NKc0cZVulcenGG2+sXfvYY491sSXqhnb3hHcEvgJsB+wKTACujIi1murmABsCU6vbvqNspyT1\npbb2hJv3ZiPiIOBBYDpwbcNdT6WUHhp16ySpz422T3gykIBlTdNnRMSSiPhVRMyKiPVG+TyS1JdG\nfCnLiAjgFODalNKdDXfNAS4FFgKvAk4EZkfEW1JKaTSNlaR+M5rrCc8CtgR2aJyYUrqo4c87IuI2\n4G5gBnD1KJ5PkvrOiLojIuKrwExgRkrpgaFqU0oLgaXAtJE8lyT1s7b3hKsA3gvYKaX0uxr1GwNT\ngCHDWpLGo7b2hCNiFrAf8CFgeURsWN0mVvevHREnRcR2EfHKiNgF+C4wH/C3VCSpSbvdER8F1gV+\nDCxquH2guv9Z4K+Ay4FfA2cANwBvTyn9qQPtlaS+0u444SFDO6X0JLDHqFokSeOIv7Ys9bgPf/jD\ntWtXrFhRu9ZTnHuDF/CRpIIMYUkqyBCWpIIMYUkqyBCWpIIMYUkqyBCWpIIMYUkqyBCWpIIMYUkq\nKEr/2EVEvBGo/3OykjR2TE8p3TRUgXvCklSQISxJBRnCklSQISxJBRnCklSQISxJBRnCklSQISxJ\nBRnCklRQL4TwxNINkKQuGTbfeiGENyndAEnqkk2GK+iFa0dMAXYH7gWeLNoYSeqMieQAnpdSenio\nwuIhLEnjWS90R0jSuGUIS1JBhrAkFWQIS1JBhrAkFWQIS1JBhrAkFfQ/fanFVqZdr6wAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328fd9ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "45315th Training Data Label is 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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sSRUZwpJUkSEsSRUZwpJUUV+PvNfs8vDDD4+krKQNxz1hSarIEJakigxhSarI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\nYqnWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328fd35a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50433th Training Data Label is 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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wOI4eY0oj4kTKKJXXUQbvr6dsxzbXkZgJJly/xko23ZZHbJ6mTctCygiI51KuV/Jw4NLY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\nZuKz6jC1PpoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2328fd99e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How does the training data look like?\n",
    "print (\"How does the training data look like?\")\n",
    "nsample = 5\n",
    "randidx = np.random.randint(trainimg.shape[0], size=nsample)\n",
    "\n",
    "for i in randidx:\n",
    "    curr_img   = np.reshape(trainimg[i, :], (28, 28)) # 28 by 28 matrix \n",
    "    curr_label = np.argmax(trainlabel[i, :] ) # Label\n",
    "    plt.matshow(curr_img, cmap=plt.get_cmap('gray'))\n",
    "    plt.title(\"\" + str(i) + \"th Training Data \" \n",
    "              + \"Label is \" + str(curr_label))\n",
    "    print (\"\" + str(i) + \"th Training Data \" \n",
    "           + \"Label is \" + str(curr_label))\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch Learning? \n",
      "type of 'batch_xs' is <class 'numpy.ndarray'>\n",
      "type of 'batch_ys' is <class 'numpy.ndarray'>\n",
      "shape of 'batch_xs' is (100, 784)\n",
      "shape of 'batch_ys' is (100, 10)\n"
     ]
    }
   ],
   "source": [
    "# Batch Learning? \n",
    "print (\"Batch Learning? \")\n",
    "batch_size = 100\n",
    "batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "print (\"type of 'batch_xs' is %s\" % (type(batch_xs)))\n",
    "print (\"type of 'batch_ys' is %s\" % (type(batch_ys)))\n",
    "print (\"shape of 'batch_xs' is %s\" % (batch_xs.shape,))\n",
    "print (\"shape of 'batch_ys' is %s\" % (batch_ys.shape,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
